rf309_ndimplot.py

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## \file
## \ingroup tutorial_roofit
## \notebook
## Multidimensional models: making 2/3 dimensional plots of pdfs and datasets
##
## \macro_image
## \macro_code
## \macro_output
##
## \date February 2018
## \authors Clemens Lange, Wouter Verkerke (C++ version)
 
import ROOT
 
# Create 2D model and dataset
# -----------------------------------------------------
 
# Create observables
x = ROOT.RooRealVar("x", "x", -5, 5)
y = ROOT.RooRealVar("y", "y", -5, 5)
 
# Create parameters
a0 = ROOT.RooRealVar("a0", "a0", -3.5, -5, 5)
a1 = ROOT.RooRealVar("a1", "a1", -1.5, -1, 1)
sigma = ROOT.RooRealVar("sigma", "width of gaussian", 1.5)
 
# Create interpreted function f(y) = a0 - a1*sqrt(10*abs(y))
fy = ROOT.RooFormulaVar("fy", "a0-a1*sqrt(10*abs(y))", [y, a0, a1])
 
# Create gauss(x,f(y),s)
model = ROOT.RooGaussian("model", "Gaussian with shifting mean", x, fy, sigma)
 
# Sample dataset from gauss(x,y)
data = model.generate({x, y}, 10000)
 
# Make 2D plots of data and model
# -------------------------------------------------------------
 
# Create and fill ROOT 2D histogram (20x20 bins) with contents of dataset
hh_data = data.createHistogram("x,y", x, Binning=20, YVar=dict(var=y, Binning=20))
 
# Create and fill ROOT 2D histogram (50x50 bins) with sampling of pdf
# hh_pdf = model.createHistogram("hh_model", x, Binning=50, YVar=dict(var=y, Binning=50))
hh_pdf = model.createHistogram("x,y", 50, 50)
hh_pdf.SetLineColor(ROOT.kBlue)
 
# Create 3D model and dataset
# -----------------------------------------------------
 
# Create observables
z = ROOT.RooRealVar("z", "z", -5, 5)
 
gz = ROOT.RooGaussian("gz", "gz", z, 0.0, 2.0)
model3 = ROOT.RooProdPdf("model3", "model3", [model, gz])
 
data3 = model3.generate({x, y, z}, 10000)
 
# Make 3D plots of data and model
# -------------------------------------------------------------
 
# Create and fill ROOT 2D histogram (8x8x8 bins) with contents of dataset
# hh_data3 = data3.createHistogram("hh_data3", x, Binning=8, YVar=(var=y, Binning=8), ZVar=(var=z, Binning=8))
hh_data3 = data3.createHistogram(
    "hh_data3",
    x,
    Binning=8,
    YVar=dict(var=y, Binning=8),
    ZVar=dict(var=z, Binning=8),
)
 
# Create and fill ROOT 2D histogram (20x20x20 bins) with sampling of pdf
hh_pdf3 = model3.createHistogram(
    "hh_model3",
    x,
    Binning=20,
    YVar=dict(var=y, Binning=20),
    ZVar=dict(var=z, Binning=20),
)
hh_pdf3.SetFillColor(ROOT.kBlue)
 
c1 = ROOT.TCanvas("rf309_2dimplot", "rf309_2dimplot", 800, 800)
c1.Divide(2, 2)
c1.cd(1)
ROOT.gPad.SetLeftMargin(0.15)
hh_data.GetZaxis().SetTitleOffset(1.4)
hh_data.Draw("lego")
c1.cd(2)
ROOT.gPad.SetLeftMargin(0.20)
hh_pdf.GetZaxis().SetTitleOffset(2.5)
hh_pdf.Draw("surf")
c1.cd(3)
ROOT.gPad.SetLeftMargin(0.15)
hh_data.GetZaxis().SetTitleOffset(1.4)
hh_data.Draw("box")
c1.cd(4)
ROOT.gPad.SetLeftMargin(0.15)
hh_pdf.GetZaxis().SetTitleOffset(2.5)
hh_pdf.Draw("cont3")
c1.SaveAs("rf309_2dimplot.png")
 
c2 = ROOT.TCanvas("rf309_3dimplot", "rf309_3dimplot", 800, 400)
c2.Divide(2)
c2.cd(1)
ROOT.gPad.SetLeftMargin(0.15)
hh_data3.GetZaxis().SetTitleOffset(1.4)
hh_data3.Draw("lego")
c2.cd(2)
ROOT.gPad.SetLeftMargin(0.15)
hh_pdf3.GetZaxis().SetTitleOffset(1.4)
hh_pdf3.Draw("iso")
c2.SaveAs("rf309_3dimplot.png")